In [1]:
import pandas as pd
import altair as alt
import geopandas as gpd
In [2]:
ukraine = gpd.read_file("ukraine.json")
df = pd.read_csv('population_trends.csv')
In [3]:
ukraine.head()
Out[3]:
GID_0 NAME_0 GID_1 NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1 HASC_1 geometry
0 UKR Ukraine UKR.1_1 Cherkasy Cherkas'ka Oblast'|Cherkasskaya Oblast'|Cherkassy None Oblast' Region None UA.CK MULTIPOLYGON (((31.32614 48.74507, 31.31716 48...
1 UKR Ukraine UKR.2_1 Chernihiv Chernigov|Tschernigow None Oblast' Region None UA.CH MULTIPOLYGON (((33.09283 50.50966, 33.09261 50...
2 UKR Ukraine UKR.3_1 Chernivtsi Chernivets'ka Oblast'|Chernovitskaya Oblast'|C... None Oblast' Region None UA.CV MULTIPOLYGON (((24.93280 47.72794, 24.93301 47...
3 UKR Ukraine UKR.4_1 Crimea Crimée|Criméia|Krim|Krymskaya Respublika|Respu... None Autonomous Republic Autonomous Republic None UA.KR MULTIPOLYGON (((33.79291 44.39153, 33.79465 44...
4 UKR Ukraine UKR.5_1 Dnipropetrovs'k Dnipropetrovsk|Dniepropietrovsk|Dnjepropetrowsk None Oblast' Region None UA.DP MULTIPOLYGON (((33.93176 47.48407, 33.92332 47...
In [4]:
df.head()
Out[4]:
region year rate
0 Ukraine 1989 1.7
1 Ukraine 1990 0.5
2 Ukraine 1991 -0.8
3 Ukraine 1992 -2.0
4 Ukraine 1993 -3.5
In [5]:
df_last_year = df[df['year'] == df.year.max()].reset_index(drop=True)
In [6]:
df_last_year.head()
Out[6]:
region year rate
0 Ukraine 2019 -6.6
1 Crimea 2019 NaN
2 Vinnytsya 2019 -7.9
3 Volyn 2019 -2.8
4 Dnipropetrovs'k 2019 -8.9
In [7]:
len([region for region in ukraine.NAME_1.values if region not in df_last_year.region.values])
Out[7]:
0
In [8]:
ukraine.rename({"NAME_1": "region"}, axis=1, inplace=True)
In [9]:
ukraine.drop(
    columns=[col_name for col_name in ukraine.columns if col_name not in ['region', 'geometry', 'rate']],
    inplace=True)
In [10]:
merged = ukraine.merge(df_last_year, on='region')
In [11]:
merged.head()
Out[11]:
region geometry year rate
0 Cherkasy MULTIPOLYGON (((31.32614 48.74507, 31.31716 48... 2019 -10.0
1 Chernihiv MULTIPOLYGON (((33.09283 50.50966, 33.09261 50... 2019 -12.5
2 Chernivtsi MULTIPOLYGON (((24.93280 47.72794, 24.93301 47... 2019 -3.1
3 Crimea MULTIPOLYGON (((33.79291 44.39153, 33.79465 44... 2019 NaN
4 Dnipropetrovs'k MULTIPOLYGON (((33.93176 47.48407, 33.92332 47... 2019 -8.9
In [12]:
merged.fillna(0, inplace=True)
In [13]:
domain = [merged.min()['rate'], 0, merged.max()['rate']]
range_ = ['#d73027', '#ffffbf', '#1a9850']

C:\Users\Xiaomi\AppData\Local\Temp/ipykernel_128/3637414924.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  domain = [merged.min()['rate'], 0, merged.max()['rate']]
In [14]:
map_plot = alt.Chart(merged).mark_geoshape(stroke='grey').encode(
    tooltip = ['region:N', 'rate:N'],
    color=alt.condition('datum.rate==0', 
                        alt.ColorValue('lightgrey'), 
                        alt.Color('rate:Q', 
                                    scale=alt.Scale(domain=domain, range=range_),
                                
                                    legend=alt.Legend(orient='none',
                                                      legendX=0, legendY=240,
                                                      direction='horizontal',
                                                      titleAnchor='start')))
)
In [15]:
rate_by_year = pd.melt(
    df, id_vars=['year', 'region'], 
    value_vars=['rate'])
In [16]:
rate_by_year.rename({"value": "rate"}, axis=1,inplace=True)
In [17]:
rate_by_year = rate_by_year[rate_by_year['region'] != "Ukraine"]
In [18]:
rate_by_year.head()
Out[18]:
year region variable rate
31 1989 Crimea rate 3.9
32 1990 Crimea rate 2.5
33 1991 Crimea rate 0.9
34 1992 Crimea rate -0.7
35 1993 Crimea rate -2.7
In [19]:
lines_plot = alt.Chart(rate_by_year).mark_line().encode(
    x=alt.X("year", axis=alt.Axis(format='f')),
    y="rate",
    color="region",
    tooltip = ['region', 'rate:N', 'year:N']
)
In [20]:
alt.hconcat(map_plot, lines_plot).configure_view(strokeWidth=0)
Out[20]:
In [ ]: